# Amazon SageMaker Model Monitor prebuilt container
<a name="model-monitor-pre-built-container"></a>

**Note**  
Amazon SageMaker Model Monitor is no longer open to new customers. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Model Monitor, but we do not plan to introduce new features. For more information, see [Amazon SageMaker Model Monitor availability change](model-monitor-availability-change.md). 

SageMaker AI provides a built-in image called `sagemaker-model-monitor-analyzer` that provides you with a range of model monitoring capabilities, including constraint suggestion, statistics generation, constraint validation against a baseline, and emitting Amazon CloudWatch metrics. This image is based on Spark version 3.3.0 and is built with [Deequ](https://github.com/awslabs/deequ) version 2.0.2.

**Note**  
You can not pull the built-in `sagemaker-model-monitor-analyzer` image directly. You can use the `sagemaker-model-monitor-analyzer` image when you submit a baseline processing or monitoring job using one of the AWS SDKs.

 Use the SageMaker Python SDK (see `image_uris.retrieve` in the [SageMaker AI Python SDK reference guide](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html)) to generate the ECR image URI for you, or specify the ECR image URI directly. The prebuilt image for SageMaker Model Monitor can be accessed as follows:

`{{<ACCOUNT_ID>}}.dkr.ecr.{{<REGION_NAME>}}.amazonaws.com/sagemaker-model-monitor-analyzer`

For example: `159807026194.dkr.ecr.us-west-2.amazonaws.com/sagemaker-model-monitor-analyzer`

If you are in an AWS region in China, the prebuilt images for SageMaker Model Monitor can be accessed as follows: 

`{{<ACCOUNT_ID>}}.dkr.ecr.{{<REGION_NAME>}}.amazonaws.com.rproxy.govskope.ca.cn/sagemaker-model-monitor-analyzer`

For account IDs and AWS Region names, see [Docker Registry Paths and Example Code](https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths).

To write your own analysis container, see the container contract described in [Custom monitoring schedules](model-monitor-custom-monitoring-schedules.md).

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for Amazon SageMaker. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query sagemaker` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).